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import jax
import jax.numpy as jnp
from LaughLM.config.loader import load_config
from LaughLM.model.gpt import GPTModel
from LaughLM.training.scheduler import build_scheduler
from LaughLM.training.optimizer import build_optimizer
from LaughLM.training.train_step import create_train_step
from LaughLM.model.parameter_utils import estimate_parameters
from LaughLM.utils.rng import create_rng
# ------------------------------------------------------------
# TPU v5e peak FLOPs
# ------------------------------------------------------------
TPU_V5E_PEAK_FLOPS = 1.576e15
# ------------------------------------------------------------
# Benchmark
# ------------------------------------------------------------
def benchmark(config_path: str, steps: int = 200, warmup: int = 20):
print("\nLoading config...")
config = load_config(config_path)
rng = create_rng(42)
# --------------------------------------------------------
# Build model
# --------------------------------------------------------
print("Initializing model...")
model = GPTModel(config=config)
batch_size = config.runtime.micro_batch_per_device
seq_len = config.runtime.seq_len
dummy_batch = jnp.zeros(
(batch_size, seq_len),
dtype=jnp.int32,
)
params = model.init(rng.next_key(), dummy_batch)["params"]
# --------------------------------------------------------
# Build optimizer
# --------------------------------------------------------
schedule = build_scheduler(config)
optimizer = build_optimizer(config, schedule)
opt_state = optimizer.init(params)
# --------------------------------------------------------
# Build train step
# --------------------------------------------------------
train_step = create_train_step(model, optimizer)
# --------------------------------------------------------
# Warmup (compile)
# --------------------------------------------------------
print("\nCompiling train step...")
start_compile = time.time()
params, opt_state, metrics = train_step(
params,
opt_state,
dummy_batch,
)
jax.block_until_ready(params)
compile_time = time.time() - start_compile
print(f"Compilation time: {compile_time:.2f}s")
# --------------------------------------------------------
# Warmup iterations
# --------------------------------------------------------
print(f"\nRunning {warmup} warmup steps...")
for _ in range(warmup):
params, opt_state, metrics = train_step(
params,
opt_state,
dummy_batch,
)
jax.block_until_ready(params)
# --------------------------------------------------------
# Benchmark loop
# --------------------------------------------------------
print(f"\nRunning benchmark ({steps} steps)...")
start = time.time()
for _ in range(steps):
params, opt_state, metrics = train_step(
params,
opt_state,
dummy_batch,
)
jax.block_until_ready(params)
end = time.time()
total_time = end - start
step_time = total_time / steps
# --------------------------------------------------------
# Throughput
# --------------------------------------------------------
tokens_per_step = (
config.runtime.seq_len
* config.runtime.micro_batch_per_device
* config.parallelism.data_parallel
* config.runtime.gradient_accumulation
)
tokens_per_sec = tokens_per_step / step_time
# --------------------------------------------------------
# MFU calculation
# --------------------------------------------------------
params_count = estimate_parameters(config)["total_params"]
mfu = (
6 * params_count * tokens_per_sec
/ TPU_V5E_PEAK_FLOPS
) * 100
# --------------------------------------------------------
# Report
# --------------------------------------------------------
print("\nBenchmark Results")
print("────────────────────────")
print(f"Steps: {steps}")
print(f"Step time: {step_time:.4f} s")
print(f"Tokens / step: {tokens_per_step:,}")
print(f"Tokens / sec: {tokens_per_sec:,.0f}")
print(f"Total parameters: {params_count:,}")
print(f"MFU: {mfu:.2f}%")
print("────────────────────────\n")
# ------------------------------------------------------------
# Entry
# ------------------------------------------------------------
if __name__ == "__main__":
benchmark(
config_path="configs/gpu_test.yaml",
steps=200,
warmup=20,
)
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